Advanced Graph Neural Networks: Architectures and Implementation
Chapter 1: Revisiting Graph Neural Network Foundations
Graph Representations for Machine Learning
The Message Passing Framework Revisited
Spectral Graph Theory Fundamentals for GNNs
Graph Signal Processing Concepts
Expressive Power and the WL Test
Chapter 2: Advanced Graph Neural Network Architectures
Graph Convolutional Networks (GCN)
Graph Attention Networks (GAT)
Implementing Multi-Head Attention in GATs
Graph Transformers
Advanced Spectral GNNs (ChebNet, CayleyNets)
Advanced Spatial GNNs (GraphSAGE Variants, PNA)
Comparing Architectural Choices and Trade-offs
Hands-on Practical: Implementing GAT Layers
Chapter 3: Addressing Training Complexities in GNNs
The Oversmoothing Problem
Techniques to Mitigate Oversmoothing
The Oversquashing Problem
Handling Large Graphs: Scalability Challenges
Neighborhood Sampling Techniques (GraphSAGE)
Graph Sampling Techniques (GraphSAINT, ShaDow-GNN)
Subgraph and Clustering Methods (Cluster-GCN)
Optimization Strategies for GNNs
Practice: Applying Scalable GNN Training
Chapter 4: Advanced GNN Tasks and Techniques
Handling Heterogeneous Graphs (RGCN, HAN)
Modeling Dynamic and Temporal Graphs
Knowledge Graph Embeddings with GNNs
Graph Generative Models
Self-Supervised Learning on Graphs
Graph Pooling and Readout Functions
Hands-on Practical: Heterogeneous Graph Node Classification
Chapter 5: GNN Implementation, Tooling, and Optimization
Deep Graph Library (DGL) Advanced Features
PyTorch Geometric (PyG) Advanced Features
Efficient Sparse Matrix Operations for GNNs
GPU Acceleration and Memory Management
Debugging and Visualizing GNNs
Benchmarking and Performance Tuning
Integrating GNNs into Production Systems
Hands-on Practical: Optimizing a GNN Implementation
Techniques to Mitigate Oversmoothing
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- Representation Learning on Graphs with Jumping Knowledge Networks, Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka, 2018 Proceedings of the 35th International Conference on Machine Learning (ICML), Vol. 80 (PMLR) - Introduces Jumping Knowledge Networks, an architecture that combines information from different layer depths to prevent oversmoothing and improve representation quality.
- DeepGCNs: Can GCNs Go as Deep as CNNs?, Guohao Li, Chenxin Xu, Weigang Zhang, Zhiwu Lu, 2019 Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE) DOI: 10.1109/ICCV.2019.00194 - Proposes a framework for building deep GCNs using techniques like residual connections, achieving state-of-the-art results and demonstrating the feasibility of deeper GNNs.
- PairNorm: Tackling Oversmoothing in GNNs, Lingxiao Zhao, Leman Akoglu, 2020 Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI) DOI: 10.24963/ijcai.2020/220 - Introduces PairNorm, a graph-specific normalization technique designed to explicitly address the oversmoothing problem by controlling the variance of node embeddings.
- DropEdge: Towards Deep Graph Convolutional Networks on Node Classification, Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huang, 2020 International Conference on Learning Representations (ICLR) (OpenReview) DOI: 10.48550/arXiv.1907.08632 - Proposes DropEdge, a regularization technique that randomly removes edges during training to prevent oversmoothing and improve the performance of deep GCNs.